Citation

De Luca GP, Parghi N, El Hayek R, Bloch-Elkouby S, Peterkin D, Wolfe A, Rogers ML, Galynker I. PLoS One 2024; 19(5): e0299048.

Copyright

(Copyright © 2024, Public Library of Science)

DOI

10.1371/journal.pone.0299048

PMID

38728274

PMCID

PMC11086905

Abstract

The Suicide Crisis Syndrome (SCS) describes a suicidal mental state marked by entrapment, affective disturbance, loss of cognitive control, hyperarousal, and social withdrawal that has predictive capacity for near-term suicidal behavior. The Suicide Crisis Inventory-2 (SCI-2), a reliable clinical tool that assesses SCS, lacks a short form for use in clinical settings which we sought to address with statistical analysis. To address this need, a community sample of 10,357 participants responded to an anonymous survey after which predictive performance for suicidal ideation (SI) and SI with preparatory behavior (SI-P) was measured using logistic regression, random forest, and gradient boosting algorithms. Four-fold cross-validation was used to split the dataset in 1,000 iterations. We compared rankings to the SCI-Short Form to inform the short form of the SCI-2. Logistic regression performed best in every analysis. The SI results were used to build the SCI-2-Short Form (SCI-2-SF) utilizing the two top ranking items from each SCS criterion. SHAP analysis of the SCI-2 resulted in meaningful rankings of its items. The SCI-2-SF, derived from these rankings, will be tested for predictive validity and utility in future studies.

Language: en

Keywords

*Machine Learning; *Suicidal Ideation; *Suicide Prevention; Adolescent; Adult; Aged; Female; Humans; Logistic Models; Male; Middle Aged; Suicide/psychology; Surveys and Questionnaires; Young Adult